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  • Writer's pictureNeurolabs

The Truth About Image Recognition Adoption

In the rapidly evolving world of image recognition (IR), there is a need for a fresh perspective on its adoption and how next-generation IR can help overcome challenges Consumer Packaged Goods (CPGs) brands and Field Marketing Agencies (FMAs) face in their daily work.

This article aims to shed light on the truth behind the narrative surrounding outdated retail execution technology (such as traditional IR solutions) and explain how Neurolabs presents a new and innovative way of doing things for those who have struggled with less-than-optimal technology in the past.

Why legacy IR technology has been a let-down

According to the 2023 POI State of the Industry report, 54% of companies use image recognition technology. However, while they may say they're using it, it doesn't mean they enjoy it.

This may be because Image recognition technology, in its older format, has been an industry-wide let-down, which has meant that many stakeholders deploying traditional IR solutions are struggling to remain or become competitive.

CPGs and FMAs need ways to maximise sales. But those who have yet to adopt IR technology are hesitant to try it due to the perceived high cost, time investment, and potential inaccuracy of the results.

Additionally, traditional IR is very complicated to use. Many companies believe it is difficult to scale if CPG’s choose to expand their offering. It can also take weeks or months to deploy the solution. Then, of course, companies need to pool time and resources into the technology’s maintenance and accuracy as time progresses.

In many cases, when companies do adopt IR tech, it ends up being incredibly disappointing.

Traditional IR vs. Synthetic IR
Comparison between Traditional IR and the new wave Synthetic IR.

Image recognition software over-promised and under-delivered

Ten years ago, image recognition was successful in other industries (e.g. security and surveillance, robotics and industrial automation). Hence it was assumed that it would work equally well within the CPG sector.

List of Limitations of Traditional Image Recognition

However, the CPG space is entirely different due to the sheer amount of similarities between products and their tendency to change extremely frequently. Under these conditions, legacy IR creates more problems than it was supposed to solve.

For example, the technology is not easily scalable in terms of expanding the product catalogue across multiple retail locations. For example, a traditional IR solution vendor can promise object detection accuracy of 95%+ in small scale trials. However, this level of detection, in many cases, cannot be replicated and sustained over time across larger scale deployments.

It also takes too long to implement (around three to six months for around 300-500 SKUs), giving rise to CPGs and FMA's deploying additional resources to get the system up and running.

As a result, these issues have generated a lot of scepticism from the industry, slowing down the drive to innovate new methods for solving the problems. Many CPGs are left saying, "We tried it but didn't get the results we wanted." Or, "We didn't get it to work, so we abandoned the project."

But what if it did work and did deliver results?

Reluctant image recognition adoption

Many companies use old IR tech as a "best of the worst" solution to their retail execution software needs. It's frustrating, but the other option (manually adding SKU and shelf-level KPIs to retail execution software) is considerably more tedious.

Nevertheless, for many companies, the manual way (i.e. counting number of facings and inputting the number in the SFA tool) was and continues to be perceived to be more reliable than traditional IR solutions.

In addition, other factors are holding companies back from adopting better IR solutions. These include:

  • The cost of change: Companies fear changing up their systems due to the presumed impact it will have on budgets.

  • Lack of awareness: Most companies are unaware of how sophisticated IR technology has become. They're stuck in this idea that IR doesn't work effectively and is unreliable.

  • Set-up and maintenance: Many CPGs and FMAs believe integrating new tech will interrupt current workflows, take too long to implement, consume a lot of internal time for training, etc. Maintaining a high level of performance is a scary ambition for many companies.

Until now, there have been few viable alternatives to the manual process/legacy IR.

Adopters may use legacy technology through gritted teeth or revert to old, manual methods.

When the IR tech under-delivers, it causes frustration and changes the perception of all IR solutions. For instance, unsatisfied IR customers may see IR tech as ineffective and wasting time and money. Because the technology under-delivers, it is seen more as a "nice to have" rather than a "must have" solution.

Therefore, making the business case to invest in newer IR solutions becomes very difficult; few employees will champion it internally with decision-makers, and as a result, this half-hearted adoption often leads to unsatisfactory outcomes for all involved.

Legacy image recognition is holding the industry back

The tension between CPGs and FMAs can be palpable, as both search for better IR tech to aid their performance. Despite the pressure, the fact remains that viable solutions have been hard to come by; leaving them locked in a standstill of disappointment and doubt.

To that end, legacy technology is seen as not able to solve problems but instead causes them. For example, it can take months to introduce an IR model to a new catalogue of SKUs. Plus, companies need timely and up-to-date analytics that they can rely on.

However, adopting a changed SKU into a legacy IR system can take days. Accuracy, implementation time, scalability and costs are all key factors that must be addressed.

Synthetic IR is the antidote to legacy IR

Our IR technology, Neurolabs ZIA, offers a cutting-edge solution to remedy many of the issues experienced with outmoded IR technologies. Its use of synthetic computer vision and data enables our IR technology to learn faster and with higher accuracy as it leverages a larger and more diverse data pool.

With our onboarding process, onboarding a new CPG customer takes a mere day, SKUs minutes, and a high degree of product detection a matter of hours - all with little to no training. Here's how our synthetic IR technology works:

New Generation Image Recognition Powered by Synthetic Data

Furthermore, you can easily streamline product catalogues across multiple retail locations, empowering CPG brands to grow and scale their offering. This is because our technology offers a much more cost-effective IR solution than traditional legacy technology, as it doesn't require CPGs to cover the costs of gathering real data to train IR learning algorithms.

While other IR solutions are available, ZIA is the only one powered by synthetic data - ushering in a new era of image recognition and expanding the possibilities within the retail sector.

To learn more about synthetic image recognition and how ZIA can transform your retail execution, click here.

The Future of Retail Shelf Auditing Ebook - Download eBook

At Neurolabs, we are revolutionising in-store retail performance with our advanced image recognition technology, ZIA. Our cutting-edge technology enables retailers, field marketing agencies and CPG brands to optimise store execution, enhance the customer experience, and boost revenue as we build the most comprehensive 3D asset library for product recognition in the CPG industry.


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